--- a/README.md
+++ b/README.md
@@ -1,56 +1,56 @@
-# Deep Learning Registration for Cardiac Motion Tracking
-
-## Introduction
-Deep learning network-based registration method applied on cardiac motion tracking from cardiac MR images (cMRI).
-If you use this code or part of this code, please consider citing the following papers:
-> Qiu, H., Qin, C., Le Folgoc, L., Hou, B., Schlemper, J., Rueckert, D.:   
-> **Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training**  
-> [STACOM Workshop, MICCAI 2019.](https://doi.org/10.1007/978-3-030-39074-7_20)  
-> (STACOM19 version of the code can be found in branch [`stacom19`](https://github.com/qiuhuaqi/cardiac-motion/tree/stacom19))
-
-> Qin, C., Bai, W., Schlemper, J., Petersen, S.E., Piechnik, S.K., Neubauer, S., Rueckert, D.:  
-> **Joint learning of motion estimation and segmentation for cardiac MR image sequences**  
-> [MICCAI 2018](https://doi.org/10.1007/978-3-030-00934-2_53)
-
-## Instructions
-### Dependencies
-Code developed and tested on Ubuntu 16.04 & 18.04 operating systems, using Python 3.6 and Pytorch 1.0.
-
-To install the Python dependencies, run the following in the root directory of the repo after cloning the repo:
-```
-pip3 install -r requirements.txt
-```
-CUDA and cuDNN are required (tested with CUDA `9.0.176` and cuDNN `7.1.4`). 
-The code should work with any CUDA and cuDNN versions supported by Pytorch 1.0. Please refer to Pytorch and NVIDIA websites.
-
-
-### Running
-The code works on a model-directory-basis. Training, testing and inference of a model are all based on the model directory of this model. 
-Logs, trained models, testing and inference results are all saved in the model directory. 
-
-Training:
-```
-python cardiac_motion/train.py --gpu [gpu_num] --model_dir [path_to_model_dir]
-```
-
-Testing (on the end-diastolic and end-systolic frames): 
-```
-python cardiac_motion/eval.py --gpu [gpu_num] --model_dir [path_to_model_dir] --restore_file [file_name_of_saved_model]
-```
-
-Inference (on all frames of the sequences):
-```
-python cardiac_motion/inference.py --gpu [gpu_num] --model_dir [path_to_model_dir] --data_dir [path_to_data_dir]
-```
-
-Most setting parameters related to data or model are specified in the `params.json` file, which should be supplied in the model directory. 
-This file is parsed into attributes of the object `params` in the code to pass the parameters. An example of this file is provided in the repo root directory.
-
-## Trained models
-Models trained on cardiac MR image data from the [UK Biobank Imaging Study](https://imaging.ukbiobank.ac.uk/) is available. 
-Please feel free to email us to enquire if you are interested.
-
-## Contact us
-If you have any question regarding the paper or the code, feel free to open an issue in this repo or email us at:
-huaqi.qiu15@imperial.ac.uk
-
+# Deep Learning Registration for Cardiac Motion Tracking
+
+## Introduction
+Deep learning network-based registration method applied on cardiac motion tracking from cardiac MR images (cMRI).
+If you use this code or part of this code, please consider citing the following papers:
+ Qiu, H., Qin, C., Le Folgoc, L., Hou, B., Schlemper, J., Rueckert, D.:   
+ **Deep Learning for Cardiac Motion Estimation: Supervised vs. Unsupervised Training**  
+ [STACOM Workshop, MICCAI 2019.](https://doi.org/10.1007/978-3-030-39074-7_20)  
+ (STACOM19 version of the code can be found in branch [`stacom19`](https://github.com/qiuhuaqi/cardiac-motion/tree/stacom19))
+
+ Qin, C., Bai, W., Schlemper, J., Petersen, S.E., Piechnik, S.K., Neubauer, S., Rueckert, D.:  
+ **Joint learning of motion estimation and segmentation for cardiac MR image sequences**  
+ [MICCAI 2018](https://doi.org/10.1007/978-3-030-00934-2_53)
+
+## Instructions
+### Dependencies
+Code developed and tested on Ubuntu 16.04 & 18.04 operating systems, using Python 3.6 and Pytorch 1.0.
+
+To install the Python dependencies, run the following in the root directory of the repo after cloning the repo:
+```
+pip3 install -r requirements.txt
+```
+CUDA and cuDNN are required (tested with CUDA `9.0.176` and cuDNN `7.1.4`). 
+The code should work with any CUDA and cuDNN versions supported by Pytorch 1.0. Please refer to Pytorch and NVIDIA websites.
+
+
+### Running
+The code works on a model-directory-basis. Training, testing and inference of a model are all based on the model directory of this model. 
+Logs, trained models, testing and inference results are all saved in the model directory. 
+
+Training:
+```
+python cardiac_motion/train.py --gpu [gpu_num] --model_dir [path_to_model_dir]
+```
+
+Testing (on the end-diastolic and end-systolic frames): 
+```
+python cardiac_motion/eval.py --gpu [gpu_num] --model_dir [path_to_model_dir] --restore_file [file_name_of_saved_model]
+```
+
+Inference (on all frames of the sequences):
+```
+python cardiac_motion/inference.py --gpu [gpu_num] --model_dir [path_to_model_dir] --data_dir [path_to_data_dir]
+```
+
+Most setting parameters related to data or model are specified in the `params.json` file, which should be supplied in the model directory. 
+This file is parsed into attributes of the object `params` in the code to pass the parameters. An example of this file is provided in the repo root directory.
+
+## Trained models
+Models trained on cardiac MR image data from the [UK Biobank Imaging Study](https://imaging.ukbiobank.ac.uk/) is available. 
+Please feel free to email us to enquire if you are interested.
+
+## Contact us
+If you have any question regarding the paper or the code, feel free to open an issue in this repo or email us at:
+huaqi.qiu15@imperial.ac.uk
+